Final answer:
AutoCorrelation (AutoCorr) measures the correlation of a time series with itself at different lags, while Partial AutoCorrelation (PAutoCorr) measures this correlation after accounting for the effects of the intervenient time lags, thus isolating the correlation at each individual lag.
Step-by-step explanation:
A key difference between AutoCorrelation (AutoCorr) and Partial AutoCorrelation (PAutoCorr) lies in what they measure within a time series dataset. AutoCorrelation functions (ACF) compute the correlation of a signal with itself at different lags. Essentially, ACF measures how well the present value of the series is related with its past values.
On the other hand, Partial AutoCorrelation functions (PACF) take this concept a step further by measuring the correlation between the series and its lagged values, after removing the effects that are accounted for by the intervenient time lags. This means PACF isolates the correlation between a particular lag and the current value without the influence of correlations at shorter lags.
Both ACF and PACF are crucial tools in the analysis of time series data, often used to identify the order of AutoRegressive (AR) and Moving Average (MA) processes in ARIMA modeling.